Why Most Asset Data Communication Fails (And How to Make Yours Succeed)
- JD Solomon
- 2 days ago
- 2 min read
Most asset managers claim their Computerized Maintenance Management System (CMMS) or Enterprise Asset Management System (EAMS) data is 90-95% accurate. In practice, audits and lifecycle modeling show far lower reliability. The challenge is communicating deficiencies without alienating stakeholders, while still driving improvement.
Why Pointing Out Asset Data Quality Fails
Large organizations manage tens of thousands of assets, each with multiple attributes. Resources and processes are rarely sufficient to maintain accuracy across this scale. Acknowledging the gap is difficult, but ignoring it undermines planning and risk management.
1 No One Wants to Admit It
Staff avoid admitting deficiencies to protect professional credibility. Frontline teams focus on operations and assume data changes slowly. Executives emphasize data quality but often rely on assumptions rather than verification. There is usually great pride in the number of years and the investments that have gone into creating the asset data.
2 Our Biases Trick Us
Cognitive biases reinforce overconfidence in asset data. Frontline staff fall into optimism bias, believing their efforts are sufficient. Senior leaders exhibit confirmation bias, assuming long-standing initiatives guarantee quality.
3 No Easy Solution
Data sets span decades and hundreds of thousands of records. Internal staff lack time for cleansing, while consultants lack familiarity with the equipment. Emerging practices—such as AI validation, reliability-centered maintenance, and industry standards like ISO 55000—offer partial solutions but remain resource-intensive.
How to Communicate Asset Data Quality
Apply asset data in practical planning tools. Deficiencies become visible through forecasting and prioritization exercises. This approach shifts the focus from abstract data cleanup to actionable improvement.
Develop an Asset Renewal and Replacement Forecast
Renewal and replacement (R&R) forecasts estimate reinvestment needs based on age, condition, and lifecycle. One byproduct is that R&R forecasts expose gaps in asset attributes when models fail due to inaccurate data or fail to align with observed performance. Forecasting also strengthens capital planning by linking data quality to funding decisions.
Use Asset Data in Capital Plan Development
Capital improvement plans rely on accurate inventories for prioritization and cost estimation. Incomplete or inaccurate data surfaces quickly when aligning projects with funding and strategic goals. Capital plans, therefore, validate asset data while guiding long-term investment.
Effective Communication of Asset Data Quality
Demonstrate deficiencies through valued applications like Renewal & Replacement Forecasts and Capital Plan Development. Stakeholders draw their own conclusions when results fail to meet data quality expectations. The gaps in asset data quality create a tangible improvement goal rather than an abstract data-cleansing goal.
Need help getting started? JD Solomon Inc. specializes in strategic asset and work management support—bringing clarity to what you own, its condition, and its value.
JD Solomon writes and speaks on decision-making, reliability, risk, and communication for leaders and technical professionals. His work connects technical disciplines with human understanding to help people make better decisions and build stronger systems. Learn more at www.jdsolomonsolutions.com and www.communicatingwithfinesse.com.
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